Star sensors make use of astronomical information in stars to determine attitude for spacecrafts by star image recognition. For low-cost star sensors with small field of view, fusion of observed images from multiple fields of view is performed and a novel recognition algorithm based on path optimization by randomly distributed ant colony is proposed. According to pheromone intensity, the ant colony can autonomously figure out a close optimal path without starting or ending point, rather than certifying a starting point first. Feature patterns extracted from the optimal path in guiding template and observed image after fusion are compared to perform star recognition. By the proposed algorithm, starting point for path optimization has no influence on the extracted feature pattern. Thus the star recognition rate is improved due to the higher stability of the extracted pattern. Simulations indicate that the algorithm improves recognition accuracy and robustness against noise for sensors with multiple fields of view.

中文翻译：

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多路径恒星传感器中基于路径优化的恒星识别

恒星传感器利用恒星中的天文信息，通过恒星图像识别来确定航天器的姿态。针对低视场的低成本恒星传感器，对来自多个视场的观测图像进行融合，提出了一种基于随机分布蚁群路径优化的新颖识别算法。根据信息素的强度，蚁群可以自动确定一条接近最佳路径而无需起点或终点，而不必先确定起点。比较从引导模板中的最佳路径中提取的特征图案和融合后的观察图像，以执行恒星识别。通过提出的算法，路径优化的起点对提取的特征模式没有影响。因此，由于提取的图案的更高的稳定性，提高了星星识别率。仿真表明，该算法提高了具有多个视场的传感器的识别精度和抗噪声能力。